Construction and validation of a TP53-associated immune prognostic model for gastric cancer

Utilizing machine learning to integrate single-cell and bulk RNA sequencing data for constructing and validating a novel cell adhesion molecules related prognostic model in gastric cancer

BACKGROUND

Cell adhesion molecules (CAMs) play a vital role in cell-cell interactions, immune response modulation, and tumor cell migration. However, the unique role of CAMs in gastric cancer (GC) remains largely unexplored.

METHODS

This study characterized the genetic alterations and mRNA expression of CAMs. The role of CD34, a representative molecule, was validated in 375 GC tissues. The activity of the CAM pathway was further tested using single-cell and bulk characterization. Next, data from 839 patients with GC from three cohorts was analyzed using univariate Cox and random survival forest methods to develop and validate a CAM-related prognostic model.

RESULTS

Most CAM-related genes exhibited multi-omics alterations and were associated with clinical outcomes. There was a strong correlation between increased CD34 expression and advanced clinical staging (P = 0.026), extensive vascular infiltration (P = 0.003), and unfavorable prognosis (Log-rank P = 0.022). CD34 expression was also found to be associated with postoperative chemotherapy and tumor immunotherapy response. Furthermore, the CAM pathway was significantly activated and mediated poor prognosis. Additionally, eight prognostic signature genes (PSGs) were identified in the training cohort. There was a substantial upregulation of the expression of immune checkpoints and a pronounced infiltration of immune cells in GC tissues with high PSG score, which is consistent with the prediction of increased sensitivity to immunotherapy. Moreover, 9 compounds from the CTRPv2 database and 13 from the Profiling Relative Inhibition Simultaneously in Mixture (PRISM) database were identified as potential therapeutic drugs for patients with GC with high PSG score.

CONCLUSION

Thorough understanding of CAM pathways regulation and the innovative PSG score model hold significant implications for medical diagnosis, potentially enhancing personalized treatment strategies and improving patient outcomes in GC management.

Construction and validation of a TTN mutation associated immune prognostic model for evaluating immune microenvironment and outcomes of gastric cancer: An observational study

Gastric cancer (GC) is a prevalent form of cancer worldwide, and TTN (titin) mutations are frequently observed in GC. However, the association between TTN mutations and immunotherapy for GC remains unclear, necessitating the development of novel prognostic models. The prognostic value and potential mechanisms of TTN in stomach adenocarcinoma were evaluated by TCGA (The Cancer Genome Atlas)-stomach adenocarcinoma cohort analysis, and an immune prognostic model was constructed based on TTN status. We validated it using the GSE84433 dataset. We performed Gene Set Enrichment Analysis and screened for differentially expressed genes, and used lasso (least absolute shrinkage and selection operator) regression analysis to screen for survival genes to construct a multifactorial survival model. In addition, we evaluated the relative proportions of 22 immune cells using the CIBERSORT algorithm for immunogenicity analysis. Finally, we constructed the nomogram integrating immune prognostic model and other clinical factors. GESA showed enrichment of immune-related phenotypes in patients with TTN mutations. We constructed an immune prognostic model based on 16 genes could identify gastric cancer patients with higher risk of poor prognosis. Immuno-microenvironmental analysis showed increased infiltration of naive B cells, plasma cells, and monocyte in high-risk patients. In addition, Nomo plots predicted the probability of 1-year, 3-year, and 5-year OS (overall survival) in GC patients, showing good predictive performance. In this study, we identified that TTN gene may be a potential clinical biomarker for GC and TTN mutations may be a predictor of immunotherapy in patients. We constructed and validated a new model for prognosis of GC patients based on immune characteristics associated with TTN mutations. This study may provide potential therapeutic strategies for gastric cancer.

New gene signature from the dominant infiltration immune cell type in osteosarcoma predicts overall survival

The immune microenvironment of osteosarcoma (OS) has been reported to play an important role in disease progression and prognosis. However, owing to tumor heterogeneity, it is not ideal to predict OS prognosis by examining only infiltrating immune cells. This work aimed to build a prognostic gene signature based on similarities in the immune microenvironments of OS patients. Public datasets were used to examine the correlated genes, and the most consistent dominant infiltrating immune cell type was identified. The LASSO Cox regression model was used to establish a multiple-gene risk prediction signature. A nine-gene prognostic signature was generated from the correlated genes for M0 macrophages and then proven to be effective and reliable in validation cohorts. Signature comparison indicated the priority of the signature. Multivariate Cox regression models indicated that the signature risk score is an independent prognostic factor for OS patients regardless of the Huvos grade in all datasets. In addition, the results of the association between the signature risk score and chemotherapy sensitivity also showed that there was no significant difference in the sensitivity of any drugs between the low- and high-risk groups. A GSEA of GO and KEGG pathways found that antigen processing- and presentation-related biological functions and olfactory transduction receptor signaling pathways have important roles in signature functioning. Our findings showed that M0 macrophages were the dominant infiltrating immune cell type in OS and that the new gene signature is a promising prognostic model for OS patients.

A Comprehensive Review of Prognostic Factors in Patients with Gastric Adenocarcinoma

Gastric adenocarcinoma remains associated with a poor long-term survival, despite recent therapeutical advances. In most parts of the world where systematic screening programs do not exist, diagnosis is often made at advanced stages, affecting long-term prognosis. In recent years, there is increasing evidence that a large bundle of factors, ranging from the tumor microenvironment to patient ethnicity and variations in therapeutic strategy, play an important role in patient outcome. A more thorough understanding of these multi-faceted parameters is needed in order to provide a better assessment of long-term prognosis in these patients, which probably also require the refinement of current staging systems. This study aims to review existing knowledge on the clinical, biomolecular and treatment-related parameters that have some prognostic value in patients with gastric adenocarcinoma.

PIK3CA somatic mutations as potential biomarker for immunotherapy in elder or TP53 mutated gastric cancer patients

Immune checkpoint inhibitors (ICIs) improve overall survival in patients with advanced gastric cancer (GC). However, the molecular characterization of GC in ICIs responders is unclear. A total of 288 advanced GC patients were included in this study. Next-generation sequencing analysis was performed on tumor tissue and paired blood to screen for somatic mutants in 639 tumor-associated genes. We demonstrated that ARID1A, HER2/3/4, KMT2C/2D, LRP1B, PIK3CA, SPTA1, and TP53 mutations were significantly correlated with high tumor mutation burden (TMB) score, as well as HER2 amplification. For HER2 and PIK3CA mutations types, this relationship was statistically significant with age and TP53 mutation status, which was also found in the CDH1 gene. These results were confirmed by sequencing 873 GC cases in the cBioPortal database. PIK3CA mutations appear to be associated with longer survival in elderly population and TP53 mutant subtypes. For the first time, we found that GC patients ≥60 years old or with TP53 mutated type and PIK3CA mutations were associated with higher TMB and better ICI response. Building upon the age and TP53 mutation status, this study suggested a novel stratification approach to GC patients and explored the correlations between genetic somatic mutations and TMB score.

Activated Mast Cells Combined with NRF2 Predict Prognosis for Esophageal Cancer

Background

Esophageal cancer (EC) had the sixth-highest mortality rate of all cancers due to its poor prognosis. Immune cells and mutation genes influenced the prognosis of EC, but their combined effect on predicting EC prognosis was unknown. In this study, we comprehensively analyzed the immune cell infiltration (ICI) and mutation genes and their combined effects for predicting prognosis in EC.

Methods

The CIBERSORT and ESTIMATE algorithms were used to analyse the ICI scape based on the TCGA and GEO databases. EC tissues and pathologic sections from Huai'an, China, were used to verify the key immune cells and mutation genes and their interactions.

Results

Stromal/immune score patterns and ICI/gene had no statistical significance in overall survival (OS) (p > 0.05). The combination of ICI and tumor mutation burden (TMB) showed that the high TMB and high ICI score group had the shortest OS (p = 0.004). We recognized that the key mutation gene NRF2 was significantly different in the high/low ICI score subgroups (p = 0.002) and positivity with mast cells (MCs) (p < 0.05). Through experimental validation, we found that the MCs and activated mast cells (AC-MCs) were more infiltration in stage II/III (p = 0.032; p = 0.013) of EC patients and that NRF2 expression was upregulated in EC (p = 0.045). AC-MCs combined with NRF2 had a poor prognosis, according to survival analysis (p = 0.056) and interactive analysis (p = 0.032).

Conclusions

We presume that NRF2 combined with AC-MCs could be a marker to predict prognosis and could influence immunotherapy through regulating PD-L1 in the EC.

Necroptosis-Related Modification Patterns Depict the Tumor Microenvironment, Redox Stress Landscape, and Prognosis of Ovarian Cancer

Necroptosis is one of programmed cell death discovered recently, which involves in tumorigenesis, cancer metastasis, and immune reaction. We studied the necroptosis-related genes (NRGs) in ovarian cancer (OV) tissues using data from public databases, which separated into two NRGclusters. Patients in cluster A would have severe clinical characteristics, poor prognosis, and worse tumor microenvironment infiltration characteristics. The NRG score was achieved through the Cox analysis, along with a construction of a prognostic model. People with lower risk score would have better prognosis, lower expression of redox related genes, higher immunogenicity, and better effect on immunotherapy. In addition, the NRG score was closely related to cancer stem cell index, copy number variations, tumor mutation load, and chemosensitivity. We built a nomogram to enhance clinical application of the signature. These outcomes can help use know the function of NRGs in OV and provide new ideas for evaluating clinical outcome and developing more effective treatment protocols.

Identification of a Two-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival in Diffuse-Type Gastric Cancer

BACKGROUND

It is widely acknowledged that the molecular biological characteristics of diffuse-type gastric cancer are different from intestinal-type gastric cancer. Notwithstanding that significant progress in high-throughput sequencing technology has been made, there is a paucity of effective prognostic biomarkers for diffuse gastric cancer for clinical practice.

METHODS

We downloaded four GEO datasets (GSE22377, GSE38749, GSE47007 and GSE62254) to establish and validate a prognostic two-gene signature for diffuse gastric cancer. The TGCA-STAD dataset was used for external validation. The optimal gene signature was established by using Cox regression analysis. Receiver operating characteristic (ROC) methodology was used to find the best prognostic model. Gene set enrichment analysis was used to analyze the possible signaling pathways of the two genes (MEF2C and TRIM15).

RESULTS

A total of four differently expressed genes (DEGs) (two upregulated and two downregulated) were identified. After a comprehensive analysis, two DEGs (MEF2C and TRIM15) were utilized to construct a prognostic model. A prognostic prediction model was constructed according to T stage, N stage, M stage and the expression of MEF2C and TRIM15. The area under the time-dependent receiver operator characteristic was used to evaluate the performance of the prognosis model in the GSE62254 dataset.

CONCLUSIONS

We demonstrated that MEF2C and TRIM15 might be key genes. We also established a prognostic nomogram based on the two-gene signature that yielded a good performance for predicting overall survival in diffuse-type gastric cancer.

Potential mechanisms and prognostic model of eRNAs-regulated genes in stomach adenocarcinoma

Gastric Carcinoma is the fourth leading cause of cancer deaths worldwide, in which stomach adenocarcinoma (STAD) is the most common histological type. A growing amount of evidence has suggested the importance of enhancer RNAs (eRNAs) in the cancer. However, the potential mechanism of eRNAs in STAD remains unclear. The eRNAs-regulated genes (eRRGs) were identified through four different enhancer resources. The differentially expressed eRRGs were obtained by ‘DESeq2’ R package. The prognosis prediction model was constructed by Cox and Lasso regression analysis. The ‘ChAMP’ R package and ‘maftools’ R package were used to investigate the multi-omics characters. In this study, combining the concept of contact domain, a total of 9014 eRRGs including 4926 PCGs and 4088 lncRNAs were identified and these eRRGs showed higher and more stable expression. Besides, the functions of these genes were mainly associated with tumor-related biological processes. Then, a prognostic prediction model was constructed and the AUC values of the 1-, 3- and 5-year survival prediction reached 0.76, 0.84 and 0.84, respectively, indicating that this model has a high accuracy. Finally, the difference between high-risk group and low-risk group were investigated using multi-omics data including gene expression, DNA methylation and somatic mutations. Our study provides significant clues for the elucidation of eRNAs in STAD and may help improve the overall survival for STAD patients.

Identification of cuproptosis-related subtypes, establishment of a prognostic model and tumor immune landscape in endometrial carcinoma

Cuproptosis, the mechanism of copper-dependent cell death, is distinct from all other known forms of regulated cell death and dependents on mitochondrial respiration. Cuproptosis promises to be a novel treatment, especially for tumors resistant to conventional therapies. We investigated the changes in cuproptosis-related genes (CRGs) in endometrial cancer (EC) cohorts from the merged Gene Expression Omnibus and the Cancer Genome Atlas databases, which could be divided into three distinct CRGclusters. Patients in CRGcluster C would have higher survival probability (P = 0.007), and higher levels of tumor microenvironment (TME) cell infiltration than other CRGclusters. CRG score was calculated via the results of univariate, multivariate cox analysis and least absolute shrinkage and selection operator regression analysis. Patients were divided into two risk subgroups according to the median risk score. Low-risk patients exhibited a more favorable prognosis, higher immunogenicity, and greater immunotherapy efficacy. Besides, CRG scores were strongly correlated to copy number variation, immunophenoscore, tumor mutation load, cancer stem cell index, microsatellite instability, and chemosensitivity. The c-index of our model is 0.702, which is higher than other four published model. The results proved that our model can distinguish EC patients with high-risk and low-risk and accurately predict the prognosis of EC patients. It will provide new ideas for clinical prognosis and precise treatments.

Molecular subtypes, clinical significance, and tumor immune landscape of angiogenesis-related genes in ovarian cancer

Angiogenesis is a physiological process, where new blood vessels are formed from pre-existing vessels through the mechanism called sprouting. It plays a significant role in supporting tumor growth and is expected to provide novel therapeutic ideas for treating tumors that are resistant to conventional therapies. We investigated the expression pattern of angiogenesis-related genes (ARGs) in ovarian cancer (OV) from public databases, in which the patients could be classified into two differential ARG clusters. It was observed that patients in ARGcluster B would have a better prognosis but lower immune cell infiltration levels in the tumor microenvironment. Then ARG score was computed based on differentially expressed genes via cox analysis, which exhibited a strong correlation to copy number variation, immunophenoscore, tumor mutation load, and chemosensitivity. In addition, according to the median risk score, patients were separated into two risk subgroups, of which the low-risk group had a better prognosis, increased immunogenicity, and stronger immunotherapy efficacy. Furthermore, we constructed a prognostic nomogram and demonstrated its predictive value. These findings help us better understand the role of ARGs in OV and offer new perspectives for clinical prognosis and personalized treatment.

Crosstalk of Redox-Related Subtypes, Establishment of a Prognostic Model and Immune Responses in Endometrial Carcinoma

Simple Summary

In order to explore the role of redox as a prognostic indicator in endometrial carcinoma (EC), we detected the expression patterns of 55 redox-related genes (RRGs) in EC cohorts from public databases. Performing consensus cluster algorithm, we determined four molecular subclusters based on RRGs which had significant differences in overall survival (OS) and immune activities of EC patients. Furthermore, we developed a prognostic risk model on the basis of the redox-related subtype by stepwise Cox regression analyses. All EC patients were divided into high-risk and low-risk groups according to the median value of risk score. Our proposed model could accurately assess the clinical outcome and had favorable independent ability in EC cases. Moreover, our signature can serve as a predictor for immune status and chemotherapy sensitivity.

Abstract

Redox plays a central part in the pathogeneses and development of tumors. We comprehensively determined the expression patterns of redox-related genes (RRGs) in endometrial carcinoma (EC) cohorts from public databases and identified four different RRG-related clusters. The prognosis and the characteristics of TME cell infiltration of RRGcluster C patients were worse than those of other RRG clusters. When it comes to the gene cluster, there were great differences in clinicopathology traits and immunocyte infiltration. The RRG score was calculated by Cox analyses, and an RRG-based signature was developed. The risk score performed well in the EC cohort. Samples were separated into two risk subgroups with the standard of the value of the median risk score. Low-risk patients had a better prognosis and higher immunogenicity. In addition, RRG score was closely associated with immunophenoscore, microsatellite instability, tumor mutation burden, tumor stem cell index, copy number variation and chemotherapy sensitivity. The nomogram accurately predicted the prognosis of patients, and our model showed better performance than other published models. In conclusion, we built a prognostic model of RRGs which can help to evaluate clinical outcomes and guide more effective treatment.

TGFβ-Associated Signature Predicts Prognosis and Tumor Microenvironment Infiltration Characterization in Gastric Carcinoma

Background: Gastric carcinoma (GC) is a carcinoma with a high incidence rate, and it is a deadly carcinoma globally. An effective tool, that is, able to predict different survival outcomes for GC patients receiving individualized treatments is deeply needed.

Methods: In total, data from 975 GC patients were collected from TCGA-STAD, GSE15459, and GSE84437. Then, we performed a comprehensive unsupervised clustering analysis based on 54 TGFβ-pathway-related genes and correlated these patterns with tumor microenvironment (TME) cell-infiltrating characteristics. WGCNA was then applied to find the module that had the closest relation with these patterns. The least absolute shrinkage and selection operator (LASSO) algorithm was combined with cross validation to narrow down variables and random survival forest (RSF) was used to create a risk score.

Results: We identified two different TGFβ regulation patterns and named them as TGFβ Cluster 1 and Cluster 2. TGFβ Cluster 1 was linked to significantly poorer survival outcomes and represented an inflamed TME subtype of GC. Using WGCNA, a module (magenta) with the closest association with the TGFβ clusters was identified. After narrowing down the gene list by univariate Cox regression analysis, the LASSO algorithm and cross validation, four of the 243 genes in the magenta module were applied to build a risk score. The group with a higher risk score exhibited a considerably poorer survival outcome with high predictive accuracy. The risk score remained an independent risk factor in multivariate Cox analysis. Moreover, we validated this risk score using GSE15459 and GSE84437. Furthermore, we found that the group with a higher risk score represented an inflamed TME according to the evidence that the risk score was remarkably correlated with several steps of cancer immunity cycles and a majority of the infiltrating immune cells. Consistently, the risk score was significantly related to immune checkpoint genes and T cell–inflamed gene expression profiles (GEPs), indicating the value of predicting immunotherapy.

Conclusions: We have developed and validated a TGFβ-associated signature, that is, capable of predicting the survival outcome as well as depicting the TME immune characteristics of GC. In summary, this signature may contribute to precision medicine for GC.

Identification of a seven-cell cycle signature predicting overall survival for gastric cancer

While genetic alterations in several regulators of the cell cycle have a significant impact on the gastric carcinogenesis process, the prognostic role of them remains to be further elucidated. The TCGA-STAD training set were downloaded and the mRNA expression matrix of cell cycle genes was extracted and corrected for further analysis after taking the intersection with GSE84437 dataset. Differentially expressed mRNAs were identified between tumor and normal tissue samples in TCGA-STAD. Univariate Cox regression analysis and lasso Cox regression model established a novel seven-gene cell cycle signature (including GADD45B, TFDP1, CDC6, CDC25A, CDC7, SMC1A and MCM3) for GC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was found to be an independent prognostic factor for GC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. The signature was further validated in the GSE84437 dataset. In tissue microarray, CDC6 and MCM3 protein expression were significant differences by the immunohistochemistry-based H-score between tumor tissues and adjacent tissues, and CDC6 is an independent prognostic factor for GC. Interestingly, our GSEA revealed that low-risk patients were more related to cell cycle pathways and might benefit more from therapies targeting cell cycle. Our study identified a novel robust seven-gene cell cycle signature for GC prognosis prediction that may serve as a beneficial complement to clinicopathological staging. The signature might provide potential biomarkers for the application of cell cycle regulators to therapies and treatment response prediction.

A Risk Model of Eight Immune-Related Genes Predicting Prognostic Response to Immune Therapies for Gastric Cancer

Immune checkpoint inhibitor (ICI) treatment is considered as an innovative approach for cancers. Since not every patient responded well to ICI therapy, it is imperative to screen out novel signatures to predict prognosis. Based on 407 gastric cancer (GC) samples retrieved from The Cancer Genome Atlas (TCGA), 36 immune-related hub genes were identified by weighted gene co-expression network analysis (WGCNA), and eight of them (RNASE2, CGB5, INHBE, DUSP1, APOA1, CD36, PTGER3, CTLA4) were used to formulate the Cox regression model. The obtained risk score was proven to be significantly correlated with overall survival (OS), consistent with the consequence of the Gene Expression Omnibus (GEO) cohort (n = 433). Then, the relationship between the risk score and clinical, molecular and immune characteristics was further investigated. Results showed that the low-risk subgroup exhibited higher mutation rate, more M1 macrophages, CD8+ and CD4+ T cells infiltrating, more active MHC-I, and bias to “IFN-γ Dominant” immune type, which is consistent with our current understanding of tumor prognostic risk. Furthermore, it is suggested that our model can accurately predict 1-, 2-, and 3-year OS of GC patients, and that it was superior to other canonical models, such as TIDE and TIS. Thus, these eight genes are probably considered as potential signatures to predict prognosis and to distinguish patient benefit from ICI, serving as a guiding individualized immunotherapy.

Construction of a Prognostic Signature of 10 Autophagy-Related lncRNAs in Gastric Cancer

Background

Autophagy plays a double-edged sword role in cancers. LncRNAs could regulate cancer initiation and development at various levels. However, the role of autophagy-related lncRNAs (ARlncs) in gastric cancer (GC) remains indistinct.

Methods

GC gene expression profile and clinical data were acquired from the Cancer Genome Atlas (TCGA). The prognostic signature composed of ARlncs was established via cox regression analysis. Kaplan–Meier (K-M) survival curve was adopted to show overall survival (OS). Independence and reliability of risk signature were visualized by cox regression analysis and ROC curve. A nomogram was constructed and the reliability was analyzed by ROC curve. Immune infiltrating cells and check points were also analyzed.

Results

A prognostic signature was constructed which stratified GC patients into high- and low-risk groups according to risk score calculated via the 10 ARlncs including LINC01094, AC068790.7, AC090772.1, AC005165.1, PVT1, LINC00106, AC026368.1, AC090912.3, AC013652.1, UICLM. Patients in high-risk group showed a poor prognosis (p<0.001). Cox regression analysis showed signature was an independent prognostic factor (p<0.001). Areas under curves (AUC) of ROC for risk signature for predicting OS outweighed age, gender, grade, T, M and N, which suggested the reliability of the signature. A nomogram was constructed with risk signature, T, M, N and age and its AUC of ROC for 1-, 3-, and 5-year was 0.700, 0.730, 0.757 respectively, which showed good reliability. Macrophage M2, T cell CD8+ and T cell CD4+ memory resting had greatest difference between the two risk groups according to CIBERSORE-ABS algorithm (p<0.001). CD274 (PD-L1), PDCD1 (PD-1) and PDCD1LG2 (PD-L2) were expressed higher in the high-risk group (p<0.05), which implied that immunotherapy may be a good choice for these patients.

Conclusion

The risk signature based on 10 ARlncs can serve as an efficacious prognostic predictor and guide the immunotherapies and precise treatment for GC patients.

WGCNA, LASSO and SVM Algorithm Revealed RAC1 Correlated M0 Macrophage and the Risk Score to Predict the Survival of Hepatocellular Carcinoma Patients

Background: RAC1 is involved in the progression of HCC as a regulator, but its prognostic performance and the imbalance of immune cell infiltration mediated by it are still unclear. We aim to explore the prognostic and immune properties of RAC1 in HCC.

Methods: We separately downloaded the data related to HCC from the Cancer Genome Atlas (TCGA) and GEO database. CIBERSORT deconvolution algorithm, weighted gene co-expression network analysis (WGCNA) and LASSO algorithm participate in identifying IRGs and the construction of prognostic signatures.

Results: The study discovered that RAC1 expression was linked to the severity of HCC lesions, and that its high expression was linked to a poor prognosis. Cox analysis confirmed that RAC1 is a clinically independent prognostic marker. M0, M1 and M2 macrophages’ abundance are significantly different in HCC. We found 828 IRGs related to macrophage infiltration, and established a novel 11-gene signature with excellent prognostic performance. RAC1-based risk score and M0 macrophage has a good ability to predict overall survival.

Conclusion: The immune state of irregular macrophage infiltration may be one of the precursors to carcinogenesis. The RAC1 correlated with M0 macrophage and the risk score to show a good performance to predict the survival of HCC patients.

Network Pharmacology Study and Experimental Validation of Yiqi Huayu Decoction Inducing Ferroptosis in Gastric Cancer

Objective

This study aimed to identify the mechanism of Yiqi Huayu Decoction (YQHY) induced ferroptosis in gastric cancer (GC) by using network pharmacology and experimental validation.

Methods

The targets of YQHY, ferroptosis-related targets, and targets related to GC were derived from databases. Following the protein–protein interaction (PPI) network, the hub targets for YQHY induced ferroptosis in GC were identified. Furthermore, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were used to analyze the hub targets from a macro perspective. We verified the hub targets by molecular docking, GEPIA, HPA, and the cBioPortal database. Finally, we performed cell viability assays, quantitative real-time polymerase chain reaction (qRT-PCR), western blotting, lipid peroxidation, and GSH assays to explore the mechanism of YQHY induced ferroptosis in GC.

Results

We identified the main active compounds and hub targets: Quercetin, DIBP, DBP, Mipax, Phaseol and TP53, ATM, SMAD4, PTGS2, and ACSL4. KEGG enrichment analyses indicated that the JAK2-STAT3 signaling pathway may be a significant pathway. Molecular docking results showed that the main active compounds had a good binding activity with the hub targets. The experimental results proved that YQHY could induce ferroptosis in AGS by increasing the MDA content and reducing the GSH content. qRT–PCR and Western blot results showed that YQHY can induce ferroptosis in GC by affecting the JAK2-STAT3 pathway and the expression of ACSL4.

Conclusions

This study indicated that YQHY can induce ferroptosis in GC by affecting the JAK2–STAT3 pathway and the expression of ACSL4, and induction of ferroptosis may be one of the possible mechanisms of YQHY’s anti-recurrence and metastasis of GC.

Cisatracurium besilate enhances the TRAIL-induced apoptosis of gastric cancer cells via p53 signaling

Cisatracurium besilate is the most commonly used non-depolarizing muscle relaxant in general anesthesia and in intensive care units. Studies have indicated that the proliferation of gastric cancer (GC) cells can be restrained by cisatracurium besilate. The present study aimed to investigate the mechanism underlying the role of cisatracurium besilate in TNF-related apoptosis-inducing ligand (TRAIL)-induced GC. The AGS cell line was exposed to cisatracurium besilate, and then cell viability, colony formation and apoptosis were assessed by performing Cell Counting Kit-8, colony formation, TUNEL and Western blot assays, respectively. Furthermore, the expression levels of p53 and p53 upregulated modulator of apoptosis (PUMA) were measured by Western blotting to determine the effect of cisatracurium besilate on p53/PUMA signaling. After co-treatment with p53 inhibitor, cisatracurium besilate and pifithrin-α/TRAIL, cell apoptosis was detected. Finally, cisatracurium besilate and pifithrin-α were used to co-treat TRAIL-induced AGS cells followed by apoptosis detection. Cisatracurium besilate treatment restrained the proliferation and promoted the apoptosis of AGS cells. Cisatracurium besilate also promoted the expression of p53 and PUMA in AGS cells. Furthermore, TRAIL induced the apoptosis of AGS cells, which was aggravated by cisatracurium besilate treatment. However, pifithrin-α reversed the synergistic effects of cisatracurium besilate and TRAIL on the activities of AGS cells. Therefore, the present study suggested that cisatracurium besilate enhanced the TRAIL-induced apoptosis of GC cells via p53 signaling, and the synergistic effects of cisatracurium besilate and TRAIL may achieve maximal therapeutic efficacy in GC management.

Identification of Hub lncRNAs Along With lncRNA-miRNA-mRNA Network for Effective Diagnosis and Prognosis of Papillary Thyroid Cancer

Long noncoding RNAs (lncRNAs) play important roles in tumorigenesis and progression of different cancers and they have been potential biomarkers for cancer diagnosis and prognosis. As the most common endocrine malignancy, precise diagnosis and prognosis of papillary thyroid cancer (PTC) is of great clinical significance. Here, we aim to identify new hub lncRNAs for marking PTC and constructed prognostics signatures based on lncRNA- miRNA-mRNA competing endogenous RNAs (ceRNA) network to predict overall survival (OS) and disease-free survival (DFS) respectively. Five reliable hub lncRNAs were identified by integrating differential genes of four Gene Expression Omnibus (GEO) gene chips using the RobustRankAggreg (RRA) method. Based on differential analyses and interaction prediction, a lncRNA-mRNA co-expression network and a lncRNA-miRNA-mRNA ceRNA network were established. Then a comprehensive function characterization of the five hub lncRNAs was performed, including validation dataset testing, receiver operating characteristic (ROC) curve analysis, and functional analysis on two networks. All results suggest that these five hub lncRNAs could be potential biomarkers for marking PTC. The ceRNA network was used to identify RNAs which were associated with PTC prognosis. Two prognostic signatures were developed using univariate and step-wise multivariate Cox regression analyses and both of them were independent prognostic indicators for PTC OS and DFS. Tumor microenvironment difference analysis between high and low-risk patients showed that dendritic cells activated and macrophages M0 may be a possible target for immunotherapy of PTC. In addition, disclosing the potential drugs that may reverse the expression of hub genes may improve the prognosis of patients with PTC. Here, connectivity map (CMap) analysis indicates that three bioactive chemicals (pioglitazone, benserazide, and SB-203580) are promising therapeutic agents for PTC. So, the paper presents a comprehensive study on diagnosis, prognosis, and potential drug screening for PTC based on the five hub lncRNAs identified by us.

Comprehensive Analysis of Glycolysis-Related Genes for Prognosis, Immune Features, and Candidate Drug Development in Colon Cancer

The dysregulated expression of glycolysis-related genes (GRGs) is closely related to the occurrence of diverse tumors and regarded as a novel target of tumor therapy. However, the role of GRGs in colon cancer is unclear. We obtained 226 differential GRGs (DE-GRGs) from The Cancer Genome Atlas (TCGA) database. Cox regression analysis was used to construct a DE-GRG prognostic model, including P4HA1, PMM2, PGM2, PPARGC1A, PPP2CB, STC2, ENO3, and CHPF2. The model could accurately predict the overall survival rate of TCGA and GSE17536 patient cohorts. The risk score of the model was closely related to a variety of clinical traits and was an independent risk factor for prognosis. Enrichment analysis revealed the activation of a variety of glycolysis metabolism and immune-related signaling pathways in the high-risk group. High-risk patients displayed low expression of CD4+ memory resting T cells and resting dendritic cells and high expression of macrophages M0 compared with the expression levels in the low-risk patients. Furthermore, patients in the high-risk group had a higher tumor mutation load and tumor stem cell index and were less sensitive to a variety of chemotherapeutic drugs. Quantitative reverse transcription polymerase chain reaction and immunohistochemistry analyses validated the expression of eight GRGs in 43 paired clinical samples. This is the first multi-omics study on the GRGs of colon cancer. The establishment of the risk model may benefit the prognosis and drug treatment of patients.

Eleven immune-gene pairs signature associated with TP53 predicting the overall survival of gastric cancer: a retrospective analysis of large sample and multicenter from public database

Background

Growing attention have been paid to the relationship between TP53 and tumor immunophenotype, but there are still lacking enough search on the field of gastric cancer (GC).

Materials and methods

We identified differential expressed immune-related genes (DEIRGs) between the TP53-altered GC samples (n = 183) and without TP53-altered GC samples (n = 192) in The Cancer Genome Atlas and paired them. In the TCGA cohort (n = 350), a risk score was determined through univariate and multivariate cox regression and Lasso regression analysis. Patients were divided into two groups, high-risk and low-risk, based on the median risk score. Four independent cohorts (GSE84437,n = 431; GSE62254, n = 300; GSE15459, n = 191; GSE26901, n = 100) from the Gene Expression Omnibus (GEO) database were used to validate the reliability and universal applicability of the model.

Results

The signature contained 11 gene pairs showed good performance in predicting progression-free survival (PFS), disease-free survival (DFS), disease special survival (DSS), and the overall survival (OS) for GC patients in the TCGA cohort. The subgroup analysis showed that the signature was suitable for GC patients with different characteristics. The signature could capable of distinguish GC patients with good prognosis and poor prognosis in all four independent external validation cohorts. The high- and low-risk groups differed significantly in the proportion of several immune cell infiltration, especially for the T cells memory resting, T cells memory activated and follicular helper, and Macrophage M0, which was also related to the prognosis of GC patients.

Conclusion

The present work proposed an innovative system for evaluating the prognosis of gastric cancer. Considering its stability and general applicability, which may become a widely used tool in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02846-x.

Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma

Background: Generally, hepatocellular carcinoma (HCC) exists in an immunosuppressive microenvironment that promotes tumor evasion. Hypoxia can impact intercellular crosstalk in the tumor microenvironment. This study aimed to explore and elucidate the underlying relationship between hypoxia and immunotherapy in patients with HCC.

Methods: HCC genomic and clinicopathological datasets were obtained from The Cancer Genome Atlas (TCGA-LIHC), Gene Expression Omnibus databases (GSE14520) and International Cancer Genome Consortium (ICGC-LIRI). The TCGA-LIHC cases were divided into clusters based on single sample gene set enrichment analysis and hierarchical clustering. After identifying patients with immunosuppressive microenvironment with different hypoxic conditions, correlations between immunological characteristics and hypoxia clusters were investigated. Subsequently, a hypoxia-associated score was established by differential expression, univariable Cox regression, and lasso regression analyses. The score was verified by survival and receiver operating characteristic curve analyses. The GSE14520 cohort was used to validate the findings of immune cell infiltration and immune checkpoints expression, while the ICGC-LIRI cohort was employed to verify the hypoxia-associated score.

Results: We identified hypoxic patients with immunosuppressive HCC. This cluster exhibited higher immune cell infiltration and immune checkpoint expression in the TCGA cohort, while similar significant differences were observed in the GEO cohort. The hypoxia-associated score was composed of five genes (ephrin A3, dihydropyrimidinase like 4, solute carrier family 2 member 5, stanniocalcin 2, and lysyl oxidase). In both two cohorts, survival analysis revealed significant differences between the high-risk and low-risk groups. In addition, compared to other clinical parameters, the established score had the highest predictive performance at both 3 and 5 years in two cohorts.

Conclusion: This study provides further evidence of the link between hypoxic signals in patients and immunosuppression in HCC. Defining hypoxia-associated HCC subtypes may help reveal potential regulatory mechanisms between hypoxia and the immunosuppressive microenvironment, and our hypoxia-associated score could exhibit potential implications for future predictive models.